Machine learning service based on skills graph

Abstract

Provided are systems and methods for recommending and prioritizing jobs to pursue, as well as skills, credentials, education, and the like to obtain, targeted to both individuals and consultants, coaches, etc. who may help those individuals. The prioritizing and the recommending may be performed based on a skills graph that includes nodes representing entities, and edges annotated with shared skills information between the entities. Thus, the skills graph can be used to find entities that are similar to each other in terms of skills.

1 . A computing system comprising:
 a storage device configured to store a graph comprising a plurality of nodes corresponding to a plurality of job types and annotated edges that connect the nodes, wherein the edges are annotated with skills relationship data; and   a processor configured to
 receive a query via a software application, wherein the query comprises an identifier of a job type and identifiers of one or more skills; 
 in response to the query, execute a machine learning model on the graph based on the identifier of the job type and the identifiers of the one or more skills to identify a recommended job type; and 
 retrieve information about the recommended job type from the storage device and display the retrieved information on a user interface of the software application. 
   
     
     
         2 . The computing system of  claim 1 , wherein the processor is configured to identify a node in the graph that corresponds to the identifier of the job type, and identify a second node in the graph that corresponds to the recommended job type via the machine learning model based on an annotated edge between the node and the second node in the graph. 
     
     
         3 . The computing system of  claim 2 , wherein the processor is configured to identify the second node based on identifiers of skills that are shared between the job type and the recommended job type which are stored in annotations on the edge between the node and the second node in the graph. 
     
     
         4 . The computing system of  claim 2 , wherein the processor is configured to identify a plurality of second nodes and select a second node corresponding to the recommended job type based on how many skills are shared between the job type and the recommended job type. 
     
     
         5 . The computing system of  claim 1 , wherein the processor is configured to receive the query via a chat session between a virtual coach and the user, and display a name of the recommended job type via the chat session with the user. 
     
     
         6 . The computing system of  claim 1 , wherein the processor is configured to receive feedback about the recommended job type, and modify one or more weights between nodes in the graph based on the received feedback. 
     
     
         7 . The computing system of  claim 1 , wherein the processor is configured to execute the machine learning model to identify a plurality of recommended job types, rank the plurality of recommended job types based on skills data in the graph, retrieve information about the plurality of recommended job types, and display the retrieved information about the plurality of recommend job types based on the ranking. 
     
     
         8 . The computing system of  claim 7 , wherein the processor is configured to display a plurality of identifiers of the plurality of recommended job types, and skills gap data for the plurality of recommended job types via the user interface. 
     
     
         9 . A method comprising:
 storing a graph comprising a plurality of nodes corresponding to a plurality of job types and annotated edges that connect the nodes, wherein the edges are annotated with skills relationship data;   receiving a query via a software application, wherein the query comprises an identifier of a job type and identifiers of one or more skills;   in response to the query, executing a machine learning model on the graph based on the identifier of the job type and the identifiers of the one or more skills to identify a recommended job type; and   retrieving information about the recommended job type from the storage device and displaying the retrieved information on a user interface of the software application.   
     
     
         10 . The method of  claim 9 , wherein the executing comprises identifying a node in the graph that corresponds to the identifier of the job type, and identifying a second node in the graph that corresponds to the recommended job type via the machine learning model based on an annotated edge between the node and the second node in the graph. 
     
     
         11 . The method of  claim 10 , wherein the identifying comprises identifying the second node based on identifiers of skills that are shared between the job type and the recommended job type which are stored in annotations on the edge between the node and the second node in the graph. 
     
     
         12 . The method of  claim 10 , wherein the identifying comprises identifying a plurality of second nodes and select a second node corresponding to the recommended job type based on how many skills are shared between the job type and the recommended job type. 
     
     
         13 . The method of  claim 9 , wherein the receiving comprises receiving the query via a chat session between a virtual coach and the user, and the displaying comprises displaying a name of the recommended job type via the chat session with the user. 
     
     
         14 . The method of  claim 9 , wherein the method further comprises receiving feedback about the recommended job type, and modifying one or more weights between nodes in the graph based on the received feedback. 
     
     
         15 . The method of  claim 9 , wherein the executing comprises executing the machine learning model to identify a plurality of recommended job types and ranking the plurality of recommended job types based on skills data in the graph, and the retrieving comprises retrieving information about the plurality of recommended job types and displaying the retrieved information about the plurality of recommended job types based on the ranking. 
     
     
         16 . The method of  claim 15 , wherein the displaying comprises displaying a plurality of identifiers of the plurality of recommended job types, and skills gap data for the plurality of recommended job types via the user interface. 
     
     
         17 . A non-transitory computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising:
 storing a graph comprising a plurality of nodes corresponding to a plurality of job types and annotated edges that connect the nodes, wherein the edges are annotated with skills relationship data;   receiving a query via a software application, wherein the query comprises an identifier of a job type and identifiers of one or more skills;   in response to the query, executing a machine learning model on the graph based on the identifier of the job type and the identifiers of the one or more skills to identify a recommended job type; and   retrieving information about the recommended job type from the storage device and displaying the retrieved information on a user interface of the software application.   
     
     
         18 . The non-transitory computer-readable medium of  claim 17 , wherein the executing comprises identifying a node in the graph that corresponds to the identifier of the job type, and identifying a second node in the graph that corresponds to the recommended job type via the machine learning model based on an annotated edge between the node and the second node in the graph. 
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein the identifying comprises identifying the second node based on identifiers of skills that are shared between the job type and the recommended job type which are stored in annotations on the edge between the node and the second node in the graph. 
     
     
         20 . The non-transitory computer-readable medium of  claim 18 , wherein the identifying comprises identifying a plurality of second nodes and select a second node corresponding to the recommended job type based on how many skills are shared between the job type and the recommended job type.